Pricing Partners, the world leader in OTC derivatives pricing analytics, mathematical models and independent valuations, announced today that its award winning Price-it Library now contains a model ...

This paper generalizes the standard homoscedastic macro-finance model by allowing for stochastic volatility, using the "square root" specification of the mainstream finance literature. Empirically, ...

The traditional approach to stochastic volatility (SV) modelling begins with the specification of an SV process, typically on the grounds of its analytical tractability (see, for example, Heston, 1993 ...

Stochastic volatility represents an essential framework for understanding the dynamic uncertainty inherent in financial markets. This approach extends traditional models by recognising that volatility ...

We conclude that stochastic volatility models have a superior fit to the history of yield movements in the Government of Russia market. We also recommend that Government of Russia interest rate risk ...

Artur Sepp and Parviz Rakhmonov introduce the lognormal stochastic volatility model for the dynamics of interest rates in the single-factor Cheyette model. They show that, unlike models that mix local ...

Affine processes provide a versatile framework for modelling complex financial phenomena, ranging from interest rate dynamics to credit risk and beyond. Their defining characteristic is the affine, or ...

The process of model implementation confirms a number of important insights for interest rate modeling generally. First, model validation of historical yields is important because those yields are the ...

Download PDF More Formats on IMF eLibrary Order a Print Copy Create Citation A stochastic volatility model where volatility was driven solely by a latent variable called news was estimated for three ...

This paper reviews the general Bayesian approach to parameter estimation in stochastic volatility models with posterior computations performed by Gibbs sampling. The main purpose is to illustrate the ...